huudan123/stage2

SentenceTransformer based on huudan123/stage1

This is a sentence-transformers model finetuned from huudan123/stage1. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

• Model Type: Sentence Transformer
• Base model: huudan123/stage1
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 768 tokens
• Similarity Function: Cosine Similarity

Model Sources

• Documentation: Sentence Transformers Documentation
• Repository: Sentence Transformers on GitHub
• Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: RobertaModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("huudan123/stage2")
# Run inference
sentences = [
    'bạn tiếp_tục nhập thông_tin cơ_sở dữ_liệu',
    'bạn mọi thứ bạn bắt_đầu_từ',
    'bạn tiếp_tục bạn nhập mọi thứ',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Semantic Similarity

• Dataset: sts-dev
• Evaluated with EmbeddingSimilarityEvaluator

Metric	Value
pearson_cosine	0.7133
spearman_cosine	0.714
pearson_manhattan	0.6924
spearman_manhattan	0.6987
pearson_euclidean	0.6928
spearman_euclidean	0.6988
pearson_dot	0.6562
spearman_dot	0.6553
pearson_max	0.7133
spearman_max	0.714

Training Details

Training Dataset

Unnamed Dataset

• Size: 254,546 training samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 3 tokens mean: 14.78 tokens max: 110 tokens	min: 3 tokens mean: 14.78 tokens max: 110 tokens	min: 3 tokens mean: 10.19 tokens max: 29 tokens

• Samples:

  anchor	positive	negative
  conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý	sản_phẩm địa_lý làm kem skiming làm_việc	kem skiming hai tập_trung sản_phẩm địa_lý
  sản_phẩm địa_lý làm kem skiming làm_việc	conceptualy kem skiming hai kích_thước cơ_bản sản_phẩm địa_lý	kem skiming hai tập_trung sản_phẩm địa_lý
  bạn biết trong mùa giải tôi đoán ở mức_độ bạn bạn mất chúng đến mức_độ tiếp_theo họ quyết_định nhớ đội_ngũ cha_mẹ chiến_binh quyết_định gọi nhớ một người ba a một người đàn_ông đi đến thay_thế anh ta một người đàn_ông nào đi thay_thế anh ta	recals thực_hiện thứ sáu	anh mất mọi thứ ở mức_độ người dân nhớ

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Evaluation Dataset

Unnamed Dataset

• Size: 1,660 evaluation samples
• Columns: anchor, positive, and negative
• Approximate statistics based on the first 1000 samples:

  	anchor	positive	negative
  type	string	string	string
  details	min: 4 tokens mean: 13.54 tokens max: 51 tokens	min: 4 tokens mean: 13.54 tokens max: 51 tokens	min: 3 tokens mean: 8.78 tokens max: 22 tokens

• Samples:

  anchor	positive	negative
  anh ấy nói mẹ con về nhà	xuống xe_buýt trường anh ấy gọi mẹ	anh nói mẹ anh về nhà
  xuống xe_buýt trường anh ấy gọi mẹ	anh ấy nói mẹ con về nhà	anh nói mẹ anh về nhà
  tôi biết mình hướng tới mục_đích báo_cáo một địa_chỉ ở washington	tôi bao_giờ đến washington tôi chỉ_định ở tôi lạc cố_gắng tìm	tôi hoàn_toàn chắc_chắn tôi làm tôi đi đến washington tôi giao báo_cáo

• Loss: MultipleNegativesRankingLoss with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }
  

Training Hyperparameters

Non-Default Hyperparameters

• overwrite_output_dir: True
• eval_strategy: epoch
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• num_train_epochs: 20
• lr_scheduler_type: cosine
• warmup_ratio: 0.05
• fp16: True
• load_best_model_at_end: True
• gradient_checkpointing: True

All Hyperparameters

Click to expand

• overwrite_output_dir: True
• do_predict: False
• eval_strategy: epoch
• prediction_loss_only: True
• per_device_train_batch_size: 256
• per_device_eval_batch_size: 256
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 1
• eval_accumulation_steps: None
• learning_rate: 5e-05
• weight_decay: 0.0
• adam_beta1: 0.9
• adam_beta2: 0.999
• adam_epsilon: 1e-08
• max_grad_norm: 1.0
• num_train_epochs: 20
• max_steps: -1
• lr_scheduler_type: cosine
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.05
• warmup_steps: 0
• log_level: passive
• log_level_replica: warning
• log_on_each_node: True
• logging_nan_inf_filter: True
• save_safetensors: True
• save_on_each_node: False
• save_only_model: False
• restore_callback_states_from_checkpoint: False
• no_cuda: False
• use_cpu: False
• use_mps_device: False
• seed: 42
• data_seed: None
• jit_mode_eval: False
• use_ipex: False
• bf16: False
• fp16: True
• fp16_opt_level: O1
• half_precision_backend: auto
• bf16_full_eval: False
• fp16_full_eval: False
• tf32: None
• local_rank: 0
• ddp_backend: None
• tpu_num_cores: None
• tpu_metrics_debug: False
• debug: []
• dataloader_drop_last: False
• dataloader_num_workers: 0
• dataloader_prefetch_factor: None
• past_index: -1
• disable_tqdm: False
• remove_unused_columns: True
• label_names: None
• load_best_model_at_end: True
• ignore_data_skip: False
• fsdp: []
• fsdp_min_num_params: 0
• fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
• fsdp_transformer_layer_cls_to_wrap: None
• accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
• deepspeed: None
• label_smoothing_factor: 0.0
• optim: adamw_torch
• optim_args: None
• adafactor: False
• group_by_length: False
• length_column_name: length
• ddp_find_unused_parameters: None
• ddp_bucket_cap_mb: None
• ddp_broadcast_buffers: False
• dataloader_pin_memory: True
• dataloader_persistent_workers: False
• skip_memory_metrics: True
• use_legacy_prediction_loop: False
• push_to_hub: False
• resume_from_checkpoint: None
• hub_model_id: None
• hub_strategy: every_save
• hub_private_repo: False
• hub_always_push: False
• gradient_checkpointing: True
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• eval_on_start: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: proportional

Training Logs

Epoch	Step	Training Loss	loss	sts-dev_spearman_cosine
0	0	-	-	0.5307
0.0503	50	9.1742	-	-
0.1005	100	5.9716	-	-
0.1508	150	4.6737	-	-
0.2010	200	3.2819	-	-
0.2513	250	2.8832	-	-
0.3015	300	2.7327	-	-
0.3518	350	2.6305	-	-
0.4020	400	2.6239	-	-
0.4523	450	2.5527	-	-
0.5025	500	2.5271	-	-
0.5528	550	2.4904	-	-
0.6030	600	2.4987	-	-
0.6533	650	2.4009	-	-
0.7035	700	2.3944	-	-
0.7538	750	2.5054	-	-
0.8040	800	2.3989	-	-
0.8543	850	2.4019	-	-
0.9045	900	2.3638	-	-
0.9548	950	2.3478	-	-
1.0	995	-	3.0169	0.7322
1.0050	1000	2.4424	-	-
1.0553	1050	2.2478	-	-
1.1055	1100	2.2448	-	-
1.1558	1150	2.205	-	-
1.2060	1200	2.1811	-	-
1.2563	1250	2.1794	-	-
1.3065	1300	2.1495	-	-
1.3568	1350	2.1548	-	-
1.4070	1400	2.1299	-	-
1.4573	1450	2.1335	-	-
1.5075	1500	2.1388	-	-
1.5578	1550	2.0999	-	-
1.6080	1600	2.0859	-	-
1.6583	1650	2.0959	-	-
1.7085	1700	2.0334	-	-
1.7588	1750	2.0647	-	-
1.8090	1800	2.0261	-	-
1.8593	1850	2.0133	-	-
1.9095	1900	2.0517	-	-
1.9598	1950	2.0152	-	-
2.0	1990	-	3.1210	0.7187
2.0101	2000	1.924	-	-
2.0603	2050	1.7472	-	-
2.1106	2100	1.7485	-	-
2.1608	2150	1.7536	-	-
2.2111	2200	1.751	-	-
2.2613	2250	1.7172	-	-
2.3116	2300	1.7269	-	-
2.3618	2350	1.7352	-	-
2.4121	2400	1.7019	-	-
2.4623	2450	1.7278	-	-
2.5126	2500	1.7046	-	-
2.5628	2550	1.6962	-	-
2.6131	2600	1.6881	-	-
2.6633	2650	1.6806	-	-
2.7136	2700	1.6614	-	-
2.7638	2750	1.6918	-	-
2.8141	2800	1.6794	-	-
2.8643	2850	1.6708	-	-
2.9146	2900	1.6531	-	-
2.9648	2950	1.6236	-	-
3.0	2985	-	3.2556	0.7140

• The bold row denotes the saved checkpoint.

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.0.1
• Transformers: 4.42.4
• PyTorch: 2.3.1+cu121
• Accelerate: 0.32.1
• Datasets: 2.20.0
• Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MultipleNegativesRankingLoss

@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}